{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| default_exp models.autoformer"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Autoformer"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Autoformer model tackles the challenge of finding reliable dependencies on intricate temporal patterns of long-horizon forecasting.\n",
    "\n",
    "The architecture has the following distinctive features:\n",
    "- In-built progressive decomposition in trend and seasonal compontents based on a moving average filter.\n",
    "- Auto-Correlation mechanism that discovers the period-based dependencies by\n",
    "calculating the autocorrelation and aggregating similar sub-series based on the periodicity.\n",
    "- Classic encoder-decoder proposed by Vaswani et al. (2017) with a multi-head attention mechanism.\n",
    "\n",
    "The Autoformer model utilizes a three-component approach to define its embedding:\n",
    "- It employs encoded autoregressive features obtained from a convolution network.\n",
    "- Absolute positional embeddings obtained from calendar features are utilized."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**References**<br>\n",
    "- [Wu, Haixu, Jiehui Xu, Jianmin Wang, and Mingsheng Long. \"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting\"](https://proceedings.neurips.cc/paper/2021/hash/bcc0d400288793e8bdcd7c19a8ac0c2b-Abstract.html)<br>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Figure 1. Autoformer Architecture.](imgs_models/autoformer.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| export\n",
    "import math\n",
    "import numpy as np\n",
    "from typing import Optional\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from neuralforecast.common._modules import DataEmbedding, SeriesDecomp\n",
    "from neuralforecast.common._base_model import BaseModel\n",
    "\n",
    "from neuralforecast.losses.pytorch import MAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "import logging\n",
    "import warnings\n",
    "\n",
    "from fastcore.test import test_eq\n",
    "from nbdev.showdoc import show_doc\n",
    "from neuralforecast.common._model_checks import check_model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Auxiliary Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| export\n",
    "class AutoCorrelation(nn.Module):\n",
    "    \"\"\"\n",
    "    AutoCorrelation Mechanism with the following two phases:\n",
    "    (1) period-based dependencies discovery\n",
    "    (2) time delay aggregation\n",
    "    This block can replace the self-attention family mechanism seamlessly.\n",
    "    \"\"\"\n",
    "    def __init__(self, mask_flag=True, factor=1, scale=None, attention_dropout=0.1, output_attention=False):\n",
    "        super(AutoCorrelation, self).__init__()\n",
    "        self.factor = factor\n",
    "        self.scale = scale\n",
    "        self.mask_flag = mask_flag\n",
    "        self.output_attention = output_attention\n",
    "        self.dropout = nn.Dropout(attention_dropout)\n",
    "\n",
    "    def time_delay_agg_training(self, values, corr):\n",
    "        \"\"\"\n",
    "        SpeedUp version of Autocorrelation (a batch-normalization style design)\n",
    "        This is for the training phase.\n",
    "        \"\"\"\n",
    "        head = values.shape[1]\n",
    "        channel = values.shape[2]\n",
    "        length = values.shape[3]\n",
    "        # find top k\n",
    "        top_k = int(self.factor * math.log(length))\n",
    "        mean_value = torch.mean(torch.mean(corr, dim=1), dim=1)\n",
    "        index = torch.topk(torch.mean(mean_value, dim=0), top_k, dim=-1)[1]\n",
    "        weights = torch.stack([mean_value[:, index[i]] for i in range(top_k)], dim=-1)\n",
    "        # update corr\n",
    "        tmp_corr = torch.softmax(weights, dim=-1)\n",
    "        # aggregation\n",
    "        tmp_values = values\n",
    "        delays_agg = torch.zeros_like(values, dtype=torch.float, device=values.device)\n",
    "        for i in range(top_k):\n",
    "            pattern = torch.roll(tmp_values, -int(index[i]), -1)\n",
    "            delays_agg = delays_agg + pattern * \\\n",
    "                         (tmp_corr[:, i].unsqueeze(1).unsqueeze(1).unsqueeze(1).repeat(1, head, channel, length))\n",
    "        return delays_agg\n",
    "\n",
    "    def time_delay_agg_inference(self, values, corr):\n",
    "        \"\"\"\n",
    "        SpeedUp version of Autocorrelation (a batch-normalization style design)\n",
    "        This is for the inference phase.\n",
    "        \"\"\"\n",
    "        batch = values.shape[0]\n",
    "        head = values.shape[1]\n",
    "        channel = values.shape[2]\n",
    "        length = values.shape[3]\n",
    "        # index init\n",
    "        init_index = torch.arange(length, device=values.device).unsqueeze(0).unsqueeze(0).unsqueeze(0).repeat(batch, head, channel, 1)\n",
    "        # find top k\n",
    "        top_k = int(self.factor * math.log(length))\n",
    "        mean_value = torch.mean(torch.mean(corr, dim=1), dim=1)\n",
    "        weights = torch.topk(mean_value, top_k, dim=-1)[0]\n",
    "        delay = torch.topk(mean_value, top_k, dim=-1)[1]\n",
    "        # update corr\n",
    "        tmp_corr = torch.softmax(weights, dim=-1)\n",
    "        # aggregation\n",
    "        tmp_values = values.repeat(1, 1, 1, 2)\n",
    "        delays_agg = torch.zeros_like(values, dtype=torch.float, device=values.device)\n",
    "        for i in range(top_k):\n",
    "            tmp_delay = init_index + delay[:, i].unsqueeze(1).unsqueeze(1).unsqueeze(1).repeat(1, head, channel, length)\n",
    "            pattern = torch.gather(tmp_values, dim=-1, index=tmp_delay)\n",
    "            delays_agg = delays_agg + pattern * \\\n",
    "                         (tmp_corr[:, i].unsqueeze(1).unsqueeze(1).unsqueeze(1).repeat(1, head, channel, length))\n",
    "        return delays_agg\n",
    "\n",
    "    def time_delay_agg_full(self, values, corr):\n",
    "        \"\"\"\n",
    "        Standard version of Autocorrelation\n",
    "        \"\"\"\n",
    "        batch = values.shape[0]\n",
    "        head = values.shape[1]\n",
    "        channel = values.shape[2]\n",
    "        length = values.shape[3]\n",
    "        # index init\n",
    "        init_index = torch.arange(length, device=values.device).unsqueeze(0).unsqueeze(0).unsqueeze(0).repeat(batch, head, channel, 1)\n",
    "        # find top k\n",
    "        top_k = int(self.factor * math.log(length))\n",
    "        weights = torch.topk(corr, top_k, dim=-1)[0]\n",
    "        delay = torch.topk(corr, top_k, dim=-1)[1]\n",
    "        # update corr\n",
    "        tmp_corr = torch.softmax(weights, dim=-1)\n",
    "        # aggregation\n",
    "        tmp_values = values.repeat(1, 1, 1, 2)\n",
    "        delays_agg = torch.zeros_like(values, dtype=torch.float, device=values.device)\n",
    "        for i in range(top_k):\n",
    "            tmp_delay = init_index + delay[..., i].unsqueeze(-1)\n",
    "            pattern = torch.gather(tmp_values, dim=-1, index=tmp_delay)\n",
    "            delays_agg = delays_agg + pattern * (tmp_corr[..., i].unsqueeze(-1))\n",
    "        return delays_agg\n",
    "\n",
    "    def forward(self, queries, keys, values, attn_mask):\n",
    "        B, L, H, E = queries.shape\n",
    "        _, S, _, D = values.shape\n",
    "        if L > S:\n",
    "            zeros = torch.zeros_like(queries[:, :(L - S), :], dtype=torch.float, device=queries.device)\n",
    "            values = torch.cat([values, zeros], dim=1)\n",
    "            keys = torch.cat([keys, zeros], dim=1)\n",
    "        else:\n",
    "            values = values[:, :L, :, :]\n",
    "            keys = keys[:, :L, :, :]\n",
    "\n",
    "        # period-based dependencies\n",
    "        q_fft = torch.fft.rfft(queries.permute(0, 2, 3, 1).contiguous(), dim=-1)\n",
    "        k_fft = torch.fft.rfft(keys.permute(0, 2, 3, 1).contiguous(), dim=-1)\n",
    "        res = q_fft * torch.conj(k_fft)\n",
    "        corr = torch.fft.irfft(res, dim=-1)\n",
    "\n",
    "        # time delay agg\n",
    "        if self.training:\n",
    "            V = self.time_delay_agg_training(values.permute(0, 2, 3, 1).contiguous(), corr).permute(0, 3, 1, 2)\n",
    "        else:\n",
    "            V = self.time_delay_agg_inference(values.permute(0, 2, 3, 1).contiguous(), corr).permute(0, 3, 1, 2)\n",
    "\n",
    "        if self.output_attention:\n",
    "            return (V.contiguous(), corr.permute(0, 3, 1, 2))\n",
    "        else:\n",
    "            return (V.contiguous(), None)\n",
    "\n",
    "\n",
    "class AutoCorrelationLayer(nn.Module):\n",
    "    \"\"\"\n",
    "    Auto Correlation Layer\n",
    "    \"\"\"\n",
    "    def __init__(self, correlation, hidden_size, n_head, d_keys=None,\n",
    "                 d_values=None):\n",
    "        super(AutoCorrelationLayer, self).__init__()\n",
    "\n",
    "        d_keys = d_keys or (hidden_size // n_head)\n",
    "        d_values = d_values or (hidden_size // n_head)\n",
    "\n",
    "        self.inner_correlation = correlation\n",
    "        self.query_projection = nn.Linear(hidden_size, d_keys * n_head)\n",
    "        self.key_projection = nn.Linear(hidden_size, d_keys * n_head)\n",
    "        self.value_projection = nn.Linear(hidden_size, d_values * n_head)\n",
    "        self.out_projection = nn.Linear(d_values * n_head, hidden_size)\n",
    "        self.n_head = n_head\n",
    "\n",
    "    def forward(self, queries, keys, values, attn_mask):\n",
    "        B, L, _ = queries.shape\n",
    "        _, S, _ = keys.shape\n",
    "        H = self.n_head\n",
    "\n",
    "        queries = self.query_projection(queries).view(B, L, H, -1)\n",
    "        keys = self.key_projection(keys).view(B, S, H, -1)\n",
    "        values = self.value_projection(values).view(B, S, H, -1)\n",
    "\n",
    "        out, attn = self.inner_correlation(\n",
    "            queries,\n",
    "            keys,\n",
    "            values,\n",
    "            attn_mask\n",
    "        )\n",
    "        out = out.view(B, L, -1)\n",
    "\n",
    "        return self.out_projection(out), attn\n",
    "    \n",
    "\n",
    "class LayerNorm(nn.Module):\n",
    "    \"\"\"\n",
    "    Special designed layernorm for the seasonal part\n",
    "    \"\"\"\n",
    "    def __init__(self, channels):\n",
    "        super(LayerNorm, self).__init__()\n",
    "        self.layernorm = nn.LayerNorm(channels)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x_hat = self.layernorm(x)\n",
    "        bias = torch.mean(x_hat, dim=1).unsqueeze(1).repeat(1, x.shape[1], 1)\n",
    "        return x_hat - bias\n",
    "\n",
    "\n",
    "class EncoderLayer(nn.Module):\n",
    "    \"\"\"\n",
    "    Autoformer encoder layer with the progressive decomposition architecture\n",
    "    \"\"\"\n",
    "    def __init__(self, attention, hidden_size, conv_hidden_size=None, MovingAvg=25, dropout=0.1, activation=\"relu\"):\n",
    "        super(EncoderLayer, self).__init__()\n",
    "        conv_hidden_size = conv_hidden_size or 4 * hidden_size\n",
    "        self.attention = attention\n",
    "        self.conv1 = nn.Conv1d(in_channels=hidden_size, out_channels=conv_hidden_size, kernel_size=1, bias=False)\n",
    "        self.conv2 = nn.Conv1d(in_channels=conv_hidden_size, out_channels=hidden_size, kernel_size=1, bias=False)\n",
    "        self.decomp1 = SeriesDecomp(MovingAvg)\n",
    "        self.decomp2 = SeriesDecomp(MovingAvg)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        self.activation = F.relu if activation == \"relu\" else F.gelu\n",
    "\n",
    "    def forward(self, x, attn_mask=None):\n",
    "        new_x, attn = self.attention(\n",
    "            x, x, x,\n",
    "            attn_mask=attn_mask\n",
    "        )\n",
    "        x = x + self.dropout(new_x)\n",
    "        x, _ = self.decomp1(x)\n",
    "        y = x\n",
    "        y = self.dropout(self.activation(self.conv1(y.transpose(-1, 1))))\n",
    "        y = self.dropout(self.conv2(y).transpose(-1, 1))\n",
    "        res, _ = self.decomp2(x + y)\n",
    "        return res, attn\n",
    "\n",
    "\n",
    "class Encoder(nn.Module):\n",
    "    \"\"\"\n",
    "    Autoformer encoder\n",
    "    \"\"\"\n",
    "    def __init__(self, attn_layers, conv_layers=None, norm_layer=None):\n",
    "        super(Encoder, self).__init__()\n",
    "        self.attn_layers = nn.ModuleList(attn_layers)\n",
    "        self.conv_layers = nn.ModuleList(conv_layers) if conv_layers is not None else None\n",
    "        self.norm = norm_layer\n",
    "\n",
    "    def forward(self, x, attn_mask=None):\n",
    "        attns = []\n",
    "        if self.conv_layers is not None:\n",
    "            for attn_layer, conv_layer in zip(self.attn_layers, self.conv_layers):\n",
    "                x, attn = attn_layer(x, attn_mask=attn_mask)\n",
    "                x = conv_layer(x)\n",
    "                attns.append(attn)\n",
    "            x, attn = self.attn_layers[-1](x)\n",
    "            attns.append(attn)\n",
    "        else:\n",
    "            for attn_layer in self.attn_layers:\n",
    "                x, attn = attn_layer(x, attn_mask=attn_mask)\n",
    "                attns.append(attn)\n",
    "\n",
    "        if self.norm is not None:\n",
    "            x = self.norm(x)\n",
    "\n",
    "        return x, attns\n",
    "\n",
    "\n",
    "class DecoderLayer(nn.Module):\n",
    "    \"\"\"\n",
    "    Autoformer decoder layer with the progressive decomposition architecture\n",
    "    \"\"\"\n",
    "    def __init__(self, self_attention, cross_attention, hidden_size, c_out, conv_hidden_size=None,\n",
    "                 MovingAvg=25, dropout=0.1, activation=\"relu\"):\n",
    "        super(DecoderLayer, self).__init__()\n",
    "        conv_hidden_size = conv_hidden_size or 4 * hidden_size\n",
    "        self.self_attention = self_attention\n",
    "        self.cross_attention = cross_attention\n",
    "        self.conv1 = nn.Conv1d(in_channels=hidden_size, out_channels=conv_hidden_size, kernel_size=1, bias=False)\n",
    "        self.conv2 = nn.Conv1d(in_channels=conv_hidden_size, out_channels=hidden_size, kernel_size=1, bias=False)\n",
    "        self.decomp1 = SeriesDecomp(MovingAvg)\n",
    "        self.decomp2 = SeriesDecomp(MovingAvg)\n",
    "        self.decomp3 = SeriesDecomp(MovingAvg)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        self.projection = nn.Conv1d(in_channels=hidden_size, out_channels=c_out, kernel_size=3, stride=1, padding=1,\n",
    "                                    padding_mode='circular', bias=False)\n",
    "        self.activation = F.relu if activation == \"relu\" else F.gelu\n",
    "\n",
    "    def forward(self, x, cross, x_mask=None, cross_mask=None):\n",
    "        x = x + self.dropout(self.self_attention(\n",
    "            x, x, x,\n",
    "            attn_mask=x_mask\n",
    "        )[0])\n",
    "        x, trend1 = self.decomp1(x)\n",
    "        x = x + self.dropout(self.cross_attention(\n",
    "            x, cross, cross,\n",
    "            attn_mask=cross_mask\n",
    "        )[0])\n",
    "        x, trend2 = self.decomp2(x)\n",
    "        y = x\n",
    "        y = self.dropout(self.activation(self.conv1(y.transpose(-1, 1))))\n",
    "        y = self.dropout(self.conv2(y).transpose(-1, 1))\n",
    "        x, trend3 = self.decomp3(x + y)\n",
    "\n",
    "        residual_trend = trend1 + trend2 + trend3\n",
    "        residual_trend = self.projection(residual_trend.permute(0, 2, 1)).transpose(1, 2)\n",
    "        return x, residual_trend\n",
    "\n",
    "\n",
    "class Decoder(nn.Module):\n",
    "    \"\"\"\n",
    "    Autoformer decoder\n",
    "    \"\"\"\n",
    "    def __init__(self, layers, norm_layer=None, projection=None):\n",
    "        super(Decoder, self).__init__()\n",
    "        self.layers = nn.ModuleList(layers)\n",
    "        self.norm = norm_layer\n",
    "        self.projection = projection\n",
    "\n",
    "    def forward(self, x, cross, x_mask=None, cross_mask=None, trend=None):\n",
    "        for layer in self.layers:\n",
    "            x, residual_trend = layer(x, cross, x_mask=x_mask, cross_mask=cross_mask)\n",
    "            trend = trend + residual_trend\n",
    "\n",
    "        if self.norm is not None:\n",
    "            x = self.norm(x)\n",
    "\n",
    "        if self.projection is not None:\n",
    "            x = self.projection(x)\n",
    "        return x, trend"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Autoformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| export\n",
    "class Autoformer(BaseModel):\n",
    "    \"\"\" Autoformer\n",
    "\n",
    "    The Autoformer model tackles the challenge of finding reliable dependencies on intricate temporal patterns of long-horizon forecasting.\n",
    "\n",
    "    The architecture has the following distinctive features:\n",
    "    - In-built progressive decomposition in trend and seasonal compontents based on a moving average filter.\n",
    "    - Auto-Correlation mechanism that discovers the period-based dependencies by\n",
    "    calculating the autocorrelation and aggregating similar sub-series based on the periodicity.\n",
    "    - Classic encoder-decoder proposed by Vaswani et al. (2017) with a multi-head attention mechanism.\n",
    "\n",
    "    The Autoformer model utilizes a three-component approach to define its embedding:\n",
    "    - It employs encoded autoregressive features obtained from a convolution network.\n",
    "    - Absolute positional embeddings obtained from calendar features are utilized.\n",
    "\n",
    "    *Parameters:*<br>\n",
    "    `h`: int, forecast horizon.<br>\n",
    "    `input_size`: int, maximum sequence length for truncated train backpropagation. Default -1 uses all history.<br>\n",
    "    `futr_exog_list`: str list, future exogenous columns.<br>\n",
    "    `hist_exog_list`: str list, historic exogenous columns.<br>\n",
    "    `stat_exog_list`: str list, static exogenous columns.<br>\n",
    "    `exclude_insample_y`: bool=False, the model skips the autoregressive features y[t-input_size:t] if True.<br>\n",
    "\t`decoder_input_size_multiplier`: float = 0.5, .<br>\n",
    "    `hidden_size`: int=128, units of embeddings and encoders.<br>\n",
    "    `n_head`: int=4, controls number of multi-head's attention.<br>\n",
    "    `dropout`: float (0, 1), dropout throughout Autoformer architecture.<br>\n",
    "\t`factor`: int=3, Probsparse attention factor.<br>\n",
    "\t`conv_hidden_size`: int=32, channels of the convolutional encoder.<br>\n",
    "\t`activation`: str=`GELU`, activation from ['ReLU', 'Softplus', 'Tanh', 'SELU', 'LeakyReLU', 'PReLU', 'Sigmoid', 'GELU'].<br>\n",
    "    `encoder_layers`: int=2, number of layers for the TCN encoder.<br>\n",
    "    `decoder_layers`: int=1, number of layers for the MLP decoder.<br>\n",
    "    `MovingAvg_window`: int=25, window size for the moving average filter.<br>\n",
    "    `loss`: PyTorch module, instantiated train loss class from [losses collection](https://nixtla.github.io/neuralforecast/losses.pytorch.html).<br>\n",
    "    `valid_loss`: PyTorch module, instantiated validation loss class from [losses collection](https://nixtla.github.io/neuralforecast/losses.pytorch.html).<br>\n",
    "    `max_steps`: int=1000, maximum number of training steps.<br>\n",
    "    `learning_rate`: float=1e-3, Learning rate between (0, 1).<br>\n",
    "    `num_lr_decays`: int=-1, Number of learning rate decays, evenly distributed across max_steps.<br>\n",
    "    `early_stop_patience_steps`: int=-1, Number of validation iterations before early stopping.<br>\n",
    "    `val_check_steps`: int=100, Number of training steps between every validation loss check.<br>\n",
    "    `batch_size`: int=32, number of different series in each batch.<br>\n",
    "    `valid_batch_size`: int=None, number of different series in each validation and test batch, if None uses batch_size.<br>\n",
    "    `windows_batch_size`: int=1024, number of windows to sample in each training batch, default uses all.<br>\n",
    "    `inference_windows_batch_size`: int=1024, number of windows to sample in each inference batch.<br>\n",
    "    `start_padding_enabled`: bool=False, if True, the model will pad the time series with zeros at the beginning, by input size.<br>\n",
    "    `scaler_type`: str='robust', type of scaler for temporal inputs normalization see [temporal scalers](https://nixtla.github.io/neuralforecast/common.scalers.html).<br>\n",
    "    `random_seed`: int=1, random_seed for pytorch initializer and numpy generators.<br>\n",
    "    `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>\n",
    "    `alias`: str, optional, Custom name of the model.<br>\n",
    "    `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br>\n",
    "    `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br>\n",
    "    `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br>\n",
    "    `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br>\n",
    "    `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br>\n",
    "    `**trainer_kwargs`: int,  keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br>\n",
    "\n",
    "\t*References*<br>\n",
    "\t- [Wu, Haixu, Jiehui Xu, Jianmin Wang, and Mingsheng Long. \"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting\"](https://proceedings.neurips.cc/paper/2021/hash/bcc0d400288793e8bdcd7c19a8ac0c2b-Abstract.html)<br>\n",
    "    \"\"\"\n",
    "    # Class attributes\n",
    "    EXOGENOUS_FUTR = True\n",
    "    EXOGENOUS_HIST = False\n",
    "    EXOGENOUS_STAT = False\n",
    "    MULTIVARIATE = False    # If the model produces multivariate forecasts (True) or univariate (False)\n",
    "    RECURRENT = False       # If the model produces forecasts recursively (True) or direct (False)\n",
    "\n",
    "    def __init__(self,\n",
    "                 h: int, \n",
    "                 input_size: int,\n",
    "                 stat_exog_list = None,\n",
    "                 hist_exog_list = None,\n",
    "                 futr_exog_list = None,\n",
    "                 exclude_insample_y = False,\n",
    "                 decoder_input_size_multiplier: float = 0.5,\n",
    "                 hidden_size: int = 128, \n",
    "                 dropout: float = 0.05,\n",
    "                 factor: int = 3,\n",
    "                 n_head: int = 4,\n",
    "                 conv_hidden_size: int = 32,\n",
    "                 activation: str = 'gelu',\n",
    "                 encoder_layers: int = 2, \n",
    "                 decoder_layers: int = 1,\n",
    "                 MovingAvg_window: int = 25,\n",
    "                 loss = MAE(),\n",
    "                 valid_loss = None,\n",
    "                 max_steps: int = 5000,\n",
    "                 learning_rate: float = 1e-4,\n",
    "                 num_lr_decays: int = -1,\n",
    "                 early_stop_patience_steps: int =-1,\n",
    "                 val_check_steps: int = 100,\n",
    "                 batch_size: int = 32,\n",
    "                 valid_batch_size: Optional[int] = None,\n",
    "                 windows_batch_size = 1024,\n",
    "                 inference_windows_batch_size = 1024,\n",
    "                 start_padding_enabled = False,\n",
    "                 step_size: int = 1,\n",
    "                 scaler_type: str = 'identity',\n",
    "                 random_seed: int = 1,\n",
    "                 drop_last_loader: bool = False,\n",
    "                 alias: Optional[str] = None,\n",
    "                 optimizer = None,\n",
    "                 optimizer_kwargs = None,\n",
    "                 lr_scheduler = None,\n",
    "                 lr_scheduler_kwargs = None,\n",
    "                 dataloader_kwargs=None,\n",
    "                 **trainer_kwargs):\n",
    "        super(Autoformer, self).__init__(h=h,\n",
    "                                       input_size=input_size,\n",
    "                                       stat_exog_list=stat_exog_list,\n",
    "                                       hist_exog_list=hist_exog_list,\n",
    "                                       futr_exog_list = futr_exog_list,\n",
    "                                       exclude_insample_y = exclude_insample_y,\n",
    "                                       loss=loss,\n",
    "                                       valid_loss=valid_loss,\n",
    "                                       max_steps=max_steps,\n",
    "                                       learning_rate=learning_rate,\n",
    "                                       num_lr_decays=num_lr_decays,\n",
    "                                       early_stop_patience_steps=early_stop_patience_steps,\n",
    "                                       val_check_steps=val_check_steps,\n",
    "                                       batch_size=batch_size,\n",
    "                                       valid_batch_size=valid_batch_size,\n",
    "                                       windows_batch_size=windows_batch_size,\n",
    "                                       inference_windows_batch_size=inference_windows_batch_size,\n",
    "                                       start_padding_enabled = start_padding_enabled,\n",
    "                                       step_size=step_size,\n",
    "                                       scaler_type=scaler_type,\n",
    "                                       random_seed=random_seed,\n",
    "                                       drop_last_loader=drop_last_loader,\n",
    "                                       alias=alias,\n",
    "                                       optimizer=optimizer,\n",
    "                                       optimizer_kwargs=optimizer_kwargs,\n",
    "                                       lr_scheduler=lr_scheduler,\n",
    "                                       lr_scheduler_kwargs=lr_scheduler_kwargs,\n",
    "                                       dataloader_kwargs=dataloader_kwargs,\n",
    "                                       **trainer_kwargs)\n",
    "\n",
    "        # Architecture\n",
    "        self.label_len = int(np.ceil(input_size * decoder_input_size_multiplier))\n",
    "        if (self.label_len >= input_size) or (self.label_len <= 0):\n",
    "            raise Exception(f'Check decoder_input_size_multiplier={decoder_input_size_multiplier}, range (0,1)')\n",
    "\n",
    "        if activation not in ['relu', 'gelu']:\n",
    "            raise Exception(f'Check activation={activation}')\n",
    "        \n",
    "        self.c_out = self.loss.outputsize_multiplier\n",
    "        self.output_attention = False\n",
    "        self.enc_in = 1 \n",
    "        self.dec_in = 1\n",
    "\n",
    "        # Decomposition\n",
    "        self.decomp = SeriesDecomp(MovingAvg_window)\n",
    "\n",
    "        # Embedding\n",
    "        self.enc_embedding = DataEmbedding(c_in=self.enc_in,\n",
    "                                           exog_input_size=self.futr_exog_size,\n",
    "                                           hidden_size=hidden_size, \n",
    "                                           pos_embedding=False,\n",
    "                                           dropout=dropout)\n",
    "        self.dec_embedding = DataEmbedding(self.dec_in,\n",
    "                                           exog_input_size=self.futr_exog_size,\n",
    "                                           hidden_size=hidden_size, \n",
    "                                           pos_embedding=False,\n",
    "                                           dropout=dropout)\n",
    "\n",
    "        # Encoder\n",
    "        self.encoder = Encoder(\n",
    "            [\n",
    "                EncoderLayer(\n",
    "                    AutoCorrelationLayer(\n",
    "                        AutoCorrelation(False, factor,\n",
    "                                      attention_dropout=dropout,\n",
    "                                      output_attention=self.output_attention),\n",
    "                        hidden_size, n_head),\n",
    "                    hidden_size=hidden_size,\n",
    "                    conv_hidden_size=conv_hidden_size,\n",
    "                    MovingAvg=MovingAvg_window,\n",
    "                    dropout=dropout,\n",
    "                    activation=activation\n",
    "                ) for l in range(encoder_layers)\n",
    "            ],\n",
    "            norm_layer=LayerNorm(hidden_size)\n",
    "        )\n",
    "        # Decoder\n",
    "        self.decoder = Decoder(\n",
    "            [\n",
    "                DecoderLayer(\n",
    "                    AutoCorrelationLayer(\n",
    "                        AutoCorrelation(True, factor, attention_dropout=dropout, output_attention=False),\n",
    "                        hidden_size, n_head),\n",
    "                    AutoCorrelationLayer(\n",
    "                        AutoCorrelation(False, factor, attention_dropout=dropout, output_attention=False),\n",
    "                        hidden_size, n_head),\n",
    "                    hidden_size=hidden_size,\n",
    "                    c_out=self.c_out,\n",
    "                    conv_hidden_size=conv_hidden_size,\n",
    "                    MovingAvg=MovingAvg_window,\n",
    "                    dropout=dropout,\n",
    "                    activation=activation,\n",
    "                )\n",
    "                for l in range(decoder_layers)\n",
    "            ],\n",
    "            norm_layer=LayerNorm(hidden_size),\n",
    "            projection=nn.Linear(hidden_size, self.c_out, bias=True)\n",
    "        )\n",
    "\n",
    "    def forward(self, windows_batch):\n",
    "        # Parse windows_batch\n",
    "        insample_y    = windows_batch['insample_y']\n",
    "        futr_exog     = windows_batch['futr_exog']\n",
    "\n",
    "        # Parse inputs\n",
    "        if self.futr_exog_size > 0:\n",
    "            x_mark_enc = futr_exog[:,:self.input_size,:]\n",
    "            x_mark_dec = futr_exog[:,-(self.label_len+self.h):,:]\n",
    "        else:\n",
    "            x_mark_enc = None\n",
    "            x_mark_dec = None\n",
    "\n",
    "        x_dec = torch.zeros(size=(len(insample_y),self.h,1), device=insample_y.device)\n",
    "        x_dec = torch.cat([insample_y[:,-self.label_len:,:], x_dec], dim=1)\n",
    "\n",
    "        # decomp init\n",
    "        mean = torch.mean(insample_y, dim=1).unsqueeze(1).repeat(1, self.h, 1)\n",
    "        zeros = torch.zeros([x_dec.shape[0], self.h, x_dec.shape[2]], device=insample_y.device)\n",
    "        seasonal_init, trend_init = self.decomp(insample_y)\n",
    "        # decoder input\n",
    "        trend_init = torch.cat([trend_init[:, -self.label_len:, :], mean], dim=1)\n",
    "        seasonal_init = torch.cat([seasonal_init[:, -self.label_len:, :], zeros], dim=1)\n",
    "        # enc\n",
    "        enc_out = self.enc_embedding(insample_y, x_mark_enc)\n",
    "        enc_out, attns = self.encoder(enc_out, attn_mask=None)\n",
    "        # dec\n",
    "        dec_out = self.dec_embedding(seasonal_init, x_mark_dec)\n",
    "        seasonal_part, trend_part = self.decoder(dec_out, enc_out, x_mask=None, cross_mask=None,\n",
    "                                                 trend=trend_init)\n",
    "        # final\n",
    "        dec_out = trend_part + seasonal_part\n",
    "\n",
    "        forecast = dec_out[:, -self.h:]\n",
    "        \n",
    "        return forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "show_doc(Autoformer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "show_doc(Autoformer.fit, name='Autoformer.fit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "show_doc(Autoformer.predict, name='Autoformer.predict')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "# Unit tests for models\n",
    "logging.getLogger(\"pytorch_lightning\").setLevel(logging.ERROR)\n",
    "logging.getLogger(\"lightning_fabric\").setLevel(logging.ERROR)\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "    check_model(Autoformer, [\"airpassengers\"])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Usage Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| eval: false\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from neuralforecast import NeuralForecast\n",
    "from neuralforecast.models import Autoformer\n",
    "from neuralforecast.utils import AirPassengersPanel, AirPassengersStatic, augment_calendar_df\n",
    "\n",
    "AirPassengersPanel, calendar_cols = augment_calendar_df(df=AirPassengersPanel, freq='M')\n",
    "\n",
    "Y_train_df = AirPassengersPanel[AirPassengersPanel.ds<AirPassengersPanel['ds'].values[-12]] # 132 train\n",
    "Y_test_df = AirPassengersPanel[AirPassengersPanel.ds>=AirPassengersPanel['ds'].values[-12]].reset_index(drop=True) # 12 test\n",
    "\n",
    "model = Autoformer(h=12,\n",
    "                 input_size=24,\n",
    "                 hidden_size = 16,\n",
    "                 conv_hidden_size = 32,\n",
    "                 n_head=2,\n",
    "                 loss=MAE(),\n",
    "                 futr_exog_list=calendar_cols,\n",
    "                 scaler_type='robust',\n",
    "                 learning_rate=1e-3,\n",
    "                 max_steps=300,\n",
    "                 val_check_steps=50,\n",
    "                 early_stop_patience_steps=2)\n",
    "\n",
    "nf = NeuralForecast(\n",
    "    models=[model],\n",
    "    freq='ME'\n",
    ")\n",
    "nf.fit(df=Y_train_df, static_df=AirPassengersStatic, val_size=12)\n",
    "forecasts = nf.predict(futr_df=Y_test_df)\n",
    "\n",
    "Y_hat_df = forecasts.reset_index(drop=False).drop(columns=['unique_id','ds'])\n",
    "plot_df = pd.concat([Y_test_df, Y_hat_df], axis=1)\n",
    "plot_df = pd.concat([Y_train_df, plot_df])\n",
    "\n",
    "if model.loss.is_distribution_output:\n",
    "    plot_df = plot_df[plot_df.unique_id=='Airline1'].drop('unique_id', axis=1)\n",
    "    plt.plot(plot_df['ds'], plot_df['y'], c='black', label='True')\n",
    "    plt.plot(plot_df['ds'], plot_df['Autoformer-median'], c='blue', label='median')\n",
    "    plt.fill_between(x=plot_df['ds'][-12:], \n",
    "                    y1=plot_df['Autoformer-lo-90'][-12:].values, \n",
    "                    y2=plot_df['Autoformer-hi-90'][-12:].values,\n",
    "                    alpha=0.4, label='level 90')\n",
    "    plt.grid()\n",
    "    plt.legend()\n",
    "    plt.plot()\n",
    "else:\n",
    "    plot_df = plot_df[plot_df.unique_id=='Airline1'].drop('unique_id', axis=1)\n",
    "    plt.plot(plot_df['ds'], plot_df['y'], c='black', label='True')\n",
    "    plt.plot(plot_df['ds'], plot_df['Autoformer'], c='blue', label='Forecast')\n",
    "    plt.legend()\n",
    "    plt.grid()"
   ]
  }
 ],
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   "display_name": "python3",
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